In many applications, a determination of a movement of an object in video information, a so-called trajectory of the object, may be useful or required. For example, as an alternative to the most common time-based playback control of a video, a direct object manipulation may be used in which a user directly controls the movement of an object within the video for navigating within the video. For example, the user may track an object along its motion trajectory and the speed of the track movement determines the speed of the playback. This direct object based playback control may have several consequences for the user, for example the user may get direct control over the movements in a scene and enables the user to skip large parts of a video where the object of interest does not move. In order to realize such a direct object manipulation there is a need to know where an object is arranged or located in the different frames of the video. Many different object-tracking algorithms are existing, for example the so-called optic flow algorithm. Furthermore, a lot of algorithms are existing for estimating or guessing where an object is arranged in the presence of an occlusion, for example using motion predictions, colour matching, form matching and so on. However, object tracking algorithms based on processing of images of the video data may require huge amounts of processing power or may require large processing times. Furthermore, there may be technical problems that may hamper a satisfying user experience in connection with the above-described direct object manipulation. One of these problems is for example, when the object and thus also the motion path of the object is occluded at some times by other objects.
For example, when a user is watching a video of a football match, the user may want to navigate within the video by using the direct object manipulation and may therefore track a certain player or the ball along the corresponding motion trajectory. For example, the video may show a specific football player running and dribbling with the ball, and several other football players trying to get the ball. In this case, the other football players may partly occlude the specific football player and the ball leading to difficulties to automatically estimate the position of the ball and the specific football player with a high reliability. The result may be that the path of the ball and/or the specific football player may be cut up in several smaller paths or the tracking of the path is lost for some time. Another problem may be that two different not related paths are wrongfully joined due to an incorrect object tracking. Another problem may arise when to many object paths are present. For example, the video may comprise a crowd of marathon runners and the user wants to control the playback of the video by directly manipulating a specific runner, for example by tracking the runner's face using a touch-sensitive display on which the video data is played back. However, there is a lot of movement within the video and there are a lot of runners. The resolution and distance to the runners may make it difficult to use a face recognition algorithm to determine a path for each runner.
Therefore, there is a need for an improved method for determining motion trajectory in video data.